scholarly journals Epidemiologic Characteristics of COVID-19 in Guizhou, China

2020 ◽  
Author(s):  
Kaike Ping ◽  
Mingyu Lei ◽  
Yun Gou ◽  
Ying Tao ◽  
Yan Huang
Keyword(s):  
Incubation Period ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Demographic Characteristics ◽  
Guizhou Province ◽  
Serial Interval ◽  
Epidemiologic Characteristics

AbstractAt the end of 2019, a coronavirus disease 2019 (COVID-19) outbroke in Wuhan, China, and spread to Guizhou province on January of 2020. To acquire the epidemiologic characteristics of COVID-19 in Guizhou, China, we collected data on 162 laboratory-confirmed cases related to COVID-19. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and basic reproduction number. With an estimation of 8 days incubation period and 6 days serial interval, our results indicate that there may exist infectiousness during the incubation period for 2019-nCoV. This increases the difficulty of screening or identifying cases related to COVID-19.

scholarly journals Epidemiologic Characteristics of COVID-19 in Guizhou Province, China

2021 ◽  
Vol 15 (03) ◽  
pp. 389-397
Author(s):  
Kaike Ping ◽  
Mingyu Lei ◽  
Yun Gou ◽  
Zhongfa Tao ◽  
Guanghai Yao ◽  
...  
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Asymptomatic Carrier ◽  
Control Measures ◽  
Guizhou Province ◽  
Serial Interval ◽  
Two Peaks ◽  
Epidemiologic Characteristics ◽  
Close Contacts

Introduction: At the end of 2019, the COVID-19 broke out, and spread to Guizhou province in January of 2020. Methodology: To acquire the epidemiologic characteristics of COVID-19 in Guizhou province, we collected data from 169 laboratory-confirmed COVID-19 related cases. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and the effective reproduction number. We also presented two representative case studies in Guizhou province: Case Study 1 was an example of the asymptomatic carrier; while Case Study 2 was an example of a large and complex infection chain that involved four different regions, spanning three provinces and eight families. Results: Two peaks in the incidence distribution associated with COVID-19 in Guizhou province were related to the 6.04 days (95% CI: 5.00 – 7.10) of incubation period and 6.14±2.21 days of serial interval. We also discussed the effectiveness of the control measures based on the instantaneous effective reproduction number that was a constantly declining curve. Conclusions: As of February 2, 2020, the estimated effective reproduction number was below 1, and no new cases were reported since February 26. These showed that Guizhou Province had achieved significant progress in preventing the spread of the epidemic. The medical isolation of close contacts was consequential. Meanwhile, the asymptomatic carriers and the super-spreaders must be isolated in time, who would cause a widespread infection.

scholarly journals Transmission interval estimates suggest pre-symptomatic spread of COVID-19

2020 ◽  
Author(s):  
Lauren C. Tindale ◽  
Michelle Coombe ◽  
Jessica E. Stockdale ◽  
Emma S. Garlock ◽  
Wing Yin Venus Lau ◽  
...  
Keyword(s):  
Incubation Period ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Interval Estimates ◽  
Secondary Infections ◽  
Serial Interval ◽  
Incubation Periods ◽  
Key Variables ◽  
The Mean ◽  
Interval Distribution

AbstractBackgroundAs the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic. We determine the incubation period and serial interval distribution for transmission clusters in Singapore and in Tianjin. We infer the basic reproduction number and identify the extent of pre-symptomatic transmission.MethodsWe collected outbreak information from Singapore and Tianjin, China, reported from Jan.19-Feb.26 and Jan.21-Feb.27, respectively. We estimated incubation periods and serial intervals in both populations.ResultsThe mean incubation period was 7.1 (6.13, 8.25) days for Singapore and 9 (7.92, 10.2) days for Tianjin. Both datasets had shorter incubation periods for earlier-occurring cases. The mean serial interval was 4.56 (2.69, 6.42) days for Singapore and 4.22 (3.43, 5.01) for Tianjin. We inferred that early in the outbreaks, infection was transmitted on average 2.55 and 2.89 days before symptom onset (Singapore, Tianjin). The estimated basic reproduction number for Singapore was 1.97 (1.45, 2.48) secondary cases per infective; for Tianjin it was 1.87 (1.65, 2.09) secondary cases per infective.ConclusionsEstimated serial intervals are shorter than incubation periods in both Singapore and Tianjin, suggesting that pre-symptomatic transmission is occurring. Shorter serial intervals lead to lower estimates of R0, which suggest that half of all secondary infections should be prevented to control spread.

scholarly journals The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran

2020 ◽  
Vol 148 ◽  
Author(s):  
A. Khosravi ◽  
R. Chaman ◽  
M. Rohani-Rasaf ◽  
F. Zare ◽  
S. Mehravaran ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Early Stage ◽  
Screening Tests ◽  
The Novel ◽  
Epidemic Size ◽  
Serial Interval ◽  
Novel Coronavirus ◽  
Interval Distribution ◽  
The Basic Reproduction Number

Abstract The aim of this study was to estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud in Northeastern Iran. The R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The 30-day probable incidence and cumulative incidence were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using ‘earlyR’ and ‘projections’ packages in R software. The maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1−3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI 1.03–1.25) by the end of day 42. The expected average number of new cases in Shahroud was 9.0 ± 3.8 cases/day, which means an estimated total of 271 (95% CI: 178–383) new cases for the period between 02 April to 03 May 2020. By day 67 (27 April), the effective reproduction number (Rt), which had a descending trend and was around 1, reduced to 0.70. Based on the Rt for the last 21 days (days 46–67 of the epidemic), the prediction for 27 April to 26 May is a mean daily cases of 2.9 ± 2.0 with 87 (48–136) new cases. In order to maintain R below 1, we strongly recommend enforcing and continuing the current preventive measures, restricting travel and providing screening tests for a larger proportion of the population.

scholarly journals Estimation of the serial interval and basic reproduction number of COVID‐19 in Qom, Iran, and three other countries: A data‐driven analysis in the early phase of the outbreak

2020 ◽  
Vol 67 (6) ◽  
pp. 2860-2868 ◽  
Author(s):  
Mohammad Aghaali ◽  
Goodarz Kolifarhood ◽  
Roya Nikbakht ◽  
Hossein Mozafar Saadati ◽  
Seyed Saeed Hashemi Nazari
Keyword(s):  
Basic Reproduction Number ◽  
Early Phase ◽  
Reproduction Number ◽  
Data Driven ◽  
Serial Interval

scholarly journals Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

2020 ◽  
Author(s):  
Robert Challen ◽  
Ellen Brooks-Pollock ◽  
Krasimira Tsaneva-Atanasova ◽  
Leon Danon
Keyword(s):  
Incubation Period ◽  
Time Delays ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Model Parameters ◽  
Small Data ◽  
Generation Interval ◽  
Serial Interval ◽  
The Impact

AbstractThe serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1–6.2) and SD 4.2 (95% CrI 3.9–4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3–5.41) and SD 1.7 (95% CrI 1.0–2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

scholarly journals Global stability of an SEIR epidemic model where empirical distribution of incubation period is approximated by Coxian distribution

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Sungchan Kim ◽  
Jong Hyuk Byun ◽  
Il Hyo Jung
Keyword(s):  
Global Stability ◽  
Incubation Period ◽  
Basic Reproduction Number ◽  
Epidemic Model ◽  
Reproduction Number ◽  
Empirical Distribution ◽  
Global Dynamics ◽  
Seir Model ◽  
Coxian Distribution ◽  
Using Data

AbstractIn this work, we have developed a Coxian-distributed SEIR model when incorporating an empirical incubation period. We show that the global dynamics are completely determined by a basic reproduction number. An application of the Coxian-distributed SEIR model using data of an empirical incubation period is explored. The model may be useful for resolving the realistic intrinsic parts in classical epidemic models since Coxian distribution approximately converges to any distribution.

scholarly journals Dynamics of COVID-19 pandemic at constant and time-dependent contact rates

2020 ◽  
Vol 15 ◽  
pp. 28 ◽  
Author(s):  
Marek Kochańczyk ◽  
Frederic Grabowski ◽  
Tomasz Lipniacki
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Exponential Phase ◽  
Contact Rate ◽  
Epidemic Spread ◽  
Early Exponential Phase ◽  
Contact Rates ◽  
Serial Interval ◽  
Hypothetical Scenario ◽  
Multiplication Coefficient

We constructed a simple Susceptible−Exposed–Infectious–Removed model of the spread of COVID-19. The model is parametrised only by the average incubation period, τ, and two rate parameters: contact rate, β, and exclusion rate, γ. The rates depend on nontherapeutic interventions and determine the basic reproduction number, R0 = β/γ, and, together with τ, the daily multiplication coefficient in the early exponential phase, θ. Initial R0 determines the reduction of β required to contain the spread of the epidemic. We demonstrate that introduction of a cascade of multiple exposed states enables the model to reproduce the distributions of the incubation period and the serial interval reported by epidemiologists. Using the model, we consider a hypothetical scenario in which β is modulated solely by anticipated changes of social behaviours: first, β decreases in response to a surge of daily new cases, pressuring people to self-isolate, and then, over longer time scale, β increases as people gradually accept the risk. In this scenario, initial abrupt epidemic spread is followed by a plateau and slow regression, which, although economically and socially devastating, grants time to develop and deploy vaccine or at least limit daily cases to a manageable number.

scholarly journals The long-term dynamics of tuberculosis and other diseases with long serial intervals: implications of and for changing reproduction numbers

1998 ◽  
Vol 121 (2) ◽  
pp. 309-324 ◽  
Author(s):  
E. VYNNYCKY ◽  
P. E. M. FINE
Keyword(s):  
Infectious Disease ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
England And Wales ◽  
Transmission Potential ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Net Reproduction ◽  
Epidemiological Parameters ◽  
The Basic Reproduction Number

The net and basic reproduction numbers are among the most widely-applied concepts in infectious disease epidemiology. A net reproduction number (the average number of secondary infectious cases resulting from each case in a given population) of above 1 is conventionally associated with an increase in incidence; the basic reproduction number (defined analogously for a ‘totally susceptible’ population) provides a standard measure of the ‘transmission potential’ of an infection. Using a model of the epidemiology of tuberculosis in England and Wales since 1900, we demonstrate that these measures are difficult to apply if disease can follow reinfection, and that they lose their conventional interpretations if important epidemiological parameters, such as the rate of contact between individuals, change over the time interval between successive cases in a chain of transmission (the serial interval).The net reproduction number for tuberculosis in England and Wales appears to have been approximately 1 from 1900 until 1950, despite concurrent declines in morbidity and mortality rates, and it declined rapidly in the second half of this century. The basic reproduction number declined from about 3 in 1900, reached 2 by 1950, and first fell below 1 in about 1960. Reductions in effective contact between individuals over this period, measured in terms of the average number of individuals to whom each case could transmit the infection, meant that the conventional basic reproduction number measure (which does not consider subsequent changes in epidemiological parameters) for a given year failed to reflect the ‘actual transmission potential’ of the infection. This latter property is better described by a variant of the conventional measure which takes secular trends in contact into account. These results are relevant for the interpretation of trends in any infectious disease for which epidemiological parameters change over time periods comparable to the infectious period, incubation period or serial interval.

scholarly journals Serial interval, basic reproduction number and prediction of COVID-19 epidemic size in Jodhpur, India

2020 ◽  
Author(s):  
Suman Saurabh ◽  
Mahendra Kumar Verma ◽  
Vaishali Gautam ◽  
Akhil Goel ◽  
Manoj Kumar Gupta ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Capacity Planning ◽  
Reproduction Number ◽  
Local Level ◽  
Substantial Reduction ◽  
Control Measures ◽  
Contact Tracing ◽  
Effective Control ◽  
Epidemic Size ◽  
Serial Interval

ABSTRACTBackgroundUnderstanding the epidemiology of COVID-19 is important for design of effective control measures at local level. We aimed to estimate the serial interval and basic reproduction number for Jodhpur, India and to use it for prediction of epidemic size for next one month.MethodsContact tracing of SARS-CoV-2 infected individuals was done to obtain the serial intervals. Aggregate and instantaneous R0 values were derived and epidemic projection was done using R software v4.0.0.ResultsFrom among 79 infector-infectee pairs, the estimated median and 95 percentile values of serial interval were 5.98 days (95% CI 5.39 – 6.65) and 13.17 days (95% CI 11.27 – 15.57), respectively. The overall R0 value in the first 30 days of outbreak was 1.64 (95% CI 1.12 – 2.25) which subsequently decreased to 1.07 (95% CI 1.06 – 1.09). The instantaneous R0 value over 14 days window ranged from a peak of 3.71 (95% CI 1.85 -2.08) to 0.88 (95% CI 0.81 – 0.96) as on 24 June 2020. The projected COVID-19 case-load over next one month was 1881 individuals. Reduction of R0 from 1.17 to 1.085 could result in 23% reduction in projected epidemic size over the next one month.ConclusionAggressive testing, contact-tracing and isolation of infected individuals in Jodhpur district resulted in reduction of R0. Further strengthening of control measures could lead to substantial reduction of COVID-19 epidemic size. A data-driven strategy was found useful in surge capacity planning and guiding the public health strategy at local level.

scholarly journals Serial interval and basic reproduction number of SARS-CoV-2 Omicron variant in South Korea

2021 ◽  
Author(s):  
Dasom Kim ◽  
Jisoo Jo ◽  
Jun-Sik Lim ◽  
Sukhyun Ryu
Keyword(s):  
South Korea ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Credible Interval ◽  
Serial Interval ◽  
Community Transmission ◽  
The Mean ◽  
The Basic Reproduction Number

South Korea is experiencing the community transmission of the SARS-CoV-2 Omicron variant (B.1.1.529). We estimated that the mean of the serial interval was 2.22 days, and the basic reproduction number was 1.90 (95% Credible Interval, 1.50-2.43) for the Omicron variant outbreak in South Korea.

Sign in / Sign up

Export Citation Format

Share Document